Most Artificial Intelligence theories of language either assume a syntactic component which serves as “front end” for the rest of the system, or else reject all attempts at distinguishing modules within the comprehension system. In this paper we will present an alternative which, while keeping modularity, will account for several puzzles for typical “syntax first” theories. The major addition to this theory is a “marker passing” (or “spreading activation”) component, which operates in parallel to the normal syntactic component.

Much of the progress in the fields constituting cognitive science has been based upon the use of explicit information processing models, almost exclusively patterned after conventional serial computers. An extension of these ideas to massively parallel, connectionist models appears to offer a number of advantages. After a preliminary discussion, this paper introduces a general connectionist model and considers how it might be used in cognitive science. Among the issues addressed are: stability and noise‐sensitivity, distributed decision‐making, time and sequence problems, and (...) the representation of complex concepts. (shrink)

Noam Chomsky's book on syntactic structures is a serious attempts on the part of a linguist to construct within the tradition of scientific theory-construction ...

The computational view of mind rests on certain intuitions regarding the fundamental similarity between computation and cognition. We examine some of these intuitions and suggest that they derive from the fact that computers and human organisms are both physical systems whose behavior is correctly described as being governed by rules acting on symbolic representations. Some of the implications of this view are discussed. It is suggested that a fundamental hypothesis of this approach is that there is a natural domain of (...) human functioning that can be addressed exclusively in terms of a formal symbolic or algorithmic vocabulary or level of analysis. Much of the paper elaborates various conditions that need to be met if a literal view of mental activity as computation is to serve as the basis for explanatory theories. The coherence of such a view depends on there being a principled distinction between functions whose explanation requires that we posit internal representations and those that we can appropriately describe as merely instantiating causal physical or biological laws. In this paper the distinction is empirically grounded in a methodological criterion called the " cognitive impenetrability condition." Functions are said to be cognitively impenetrable if they cannot be influenced by such purely cognitive factors as goals, beliefs, inferences, tacit knowledge, and so on. Such a criterion makes it possible to empirically separate the fixed capacities of mind from the particular representations and algorithms used on specific occasions. In order for computational theories to avoid being ad hoc, they must deal effectively with the "degrees of freedom" problem by constraining the extent to which they can be arbitrarily adjusted post hoc to fit some particular set of observations. This in turn requires that the fixed architectural function and the algorithms be independently validated. It is argued that the architectural assumptions implicit in many contemporary models run afoul of the cognitive impenetrability condition, since the required fixed functions are demonstrably sensitive to tacit knowledge and goals. The paper concludes with some tactical suggestions for the development of computational cognitive theories. (shrink)

Most theories of memory suggest that when we learn or memorize something, some drepresentation of that something is constructed, stored and later retrieved. This raises questions like:How is information represented?How is it stored?How is it retrieved?Then, how is it used?This paper tries to deal with all these at once. When you get an idea and want to “remember” it, you create a “K‐line” for it. When later activated, the K‐line induces a partial mental state resembling the one that created it. (...) A “partial mental state” is a subset of those mental agencies operating at one moment. This view leads to many ideas about the development, structure and physiology of memory, and about how to implement framelike representations in a distributed processor. (shrink)

The paper describes a system for dealing with nestings of belief in terms of the mechanism of computational environment. A method is offered for computing the beliefs of A about B (and so on) in terms of the systems existing knowledge structures about A and B separately. A proposal for belief percolation is put forward: percolation being a side effect of the process of the computation of nested beliefs, but one which could explain the acquisition of unsupported beliefs. It is (...) argued that the mechanism proposed is compatible with a general least effort hypothesis concerning human mental functioning. (shrink)

This paper describes a computer simulation of reading that is strongly driven by eye fixation data from human readers. The simulation, READER, is a natural language understanding system that reads a text word by word and whose processing cycles on each word have some correspondence with the human gaze duration on that word. READER operates within a newly developed information processing architecture, a Collaborative, Activation‐based, Production System (CAPS) that permits the modeling of the temporal properties of human comprehension. CAPS allows (...) for concurrent, collaborative execution of processes operating at different levels of analysis. As READER encounters each successive word, the word is operated on by processes at the levels of word encoding, lexical access, syntactic and semantic analysis, and referential and schema‐level processes. Like human readers, READER uses a strategy of immediacy of comprehension, attempting to interpret each word as soon as it is encountered, rather than unnecessarily buffering information. A major contribution of this simulation is its use of human performance characteristics in constraining and determining the model's mechanisms. (shrink)